Latest AI and machine learning research in bipolar disorder for healthcare professionals.
In recent years, deep learning techniques have been extensively used for the identification and classification of lithium-ion batteries. However, these models typically require a costly and labor-intensive labeling process, often influenced by commercial or proprietary concerns. In this study, we introduce RecyBat24, a publicly accessible image dataset for the detection and classification of three...
Due to their excellent specific strength and lightweight characteristics, Al-Cu-Li alloys are widely used in aerospace applications. The newly developed three-stage creep aging (CA) process ensures both the formability and high performance of the Al alloy. However, research at the atomic scale investigating the relationship between the microstructure and performance of ternary alloys under intrica...
Ab initio molecular dynamics (AIMD) simulations have been employed to investigate doped antiperovskite solid-state electrolyte (AP SSE) structures, sp...
Coulombic efficiency (CE) is a quantifiable indicator for the reversibility of lithium metal anodes in high-energy-density batteries. However, the qua...
INTRODUCTION: Individuals at clinical high risk for bipolar disorder (CHR-BD) experienced insufficient recognition. Little is known regarding the asso...
Solid-state lithium metal batteries using garnet-type LiLaZrO electrolytes hold immense promise for next-generation energy storage, but grain boundary...
BACKGROUND: Over the last decade, there has been considerable development in precision psychiatry, especially in the development of novel prediction t...
Yu 's study has advanced the understanding of the neural mechanisms underlying major depressive disorder (MDD) in adolescents, emphasizing the signifi...
Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly incr...
Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help ...
1. The research aims to develop and validate a stability-indicating reverse phase high-performance liquid chromatography (RP-HPLC) method for Lurasido...
: A comorbidity between Borderline Personality Disorder (BPD) and Posttraumatic Stress Disorder (PTSD) is common, severely disabling, and hard to trea...
Physical activity is a modifiable factor influencing chronic disease risk. Previous studies often relied on self-reported activity measures or short-t...
In this study, we present a hybrid Physics-Assisted Machine Learning (PAML) model that integrates Deep Learning (DL) techniques with the classical Dis...
One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...
Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine ...
The advancement of battery technology necessitates a profound understanding of the physical, chemical, and electrochemical processes at various scales...
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...
The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predict...
Understanding anode failure mechanisms in lithium metal batteries (LMBs) is crucial for their use in energy storage, as the anode directly affects bat...